Visual Analytics and Imaging Laboratory (VAI Lab)
Computer Science Department, Stony Brook University, NY

SketchPadN-D: WYDIWYG Sculpting and Editing in High-Dimensional Space

Abstract: High-dimensional data visualization has been attracting much attention. To fully test related software and algorithms, researchers require a diverse pool of data with known and desired features. Test data do not always provide this, or only partially. Here we propose the paradigm WYDIWYGS (What You Draw Is What You Get). Its embodiment, SketchPadND, is a tool that allows users to generate high-dimensional data in the same interface they also use for visualization. This provides for an immersive and direct data generation activity, and furthermore it also enables users to interactively edit and clean existing high-dimensional data from possible artifacts. SketchPadND offers two visualization paradigms, one based on parallel coordinates and the other based on a relatively new framework using an N-D polygon to navigate in high-dimensional space. The first interface allows users to draw arbitrary profiles of probability density functions along each dimension axis and sketch shapes for data density and connections between adjacent dimensions. The second interface embraces the idea of sculpting. Users can carve data at arbitrary orientations and refine them wherever necessary. This guarantees that the data generated is truly high-dimensional. We demonstrate our tool’s usefulness in real data visualization scenarios.

Teaser: Below are the two interfaces: parallel coordinates and dynamic scatterplots

Teaser Image

(left) parallel coordinates interface, (right) dynamic scatterplot interface

Video: Watch it to get a quick overview:

Paper: B. Wang, P. Ruchikachorn, K. Mueller, “SketchPadN-D: WYDIWYG Sculpting and Editing in High-Dimensional Space,” IEEE Trans. on Visualization and Computer Graphics, 19(12): 2060-2069, 2013..pdf

Demo: You can play with a partial demo here. It is brower-based -- Chrome and Firefox are reommended.

Funding: NSF grant IIS-1117132